ArticleBioinformatics (Oxford, England)2025
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- A simple probabilistic AlphaFold interaction score.Protein science : a publication of the Protein Society · 2026Article
- ABAG-Rank: improving model selection of AlphaFold antibody-antigen complexes by learning to rank.Bioinformatics (Oxford, England) · 2026Article
- Human PMS1-dependent non-canonical mismatch repair engages with MBD4 to repair methylated CpG deamination.Nucleic acids research · 2026Article
- Salmonella SopB suppresses post-transcriptionally regulated cytokine release to reduce early tissue inflammation and delay disease progression.Nature communications · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- When can AlphaFold predict the oligomeric states of proteins?Protein science : a publication of the Protein Society · 2026Article
- Novel binding partners of the Vacuolar Transporter Chaperone (VTC) complex in Acidocalcisomes of Leishmania tarentolae.PLoS neglected tropical diseases · 2026Article
- A Chromatin Biology Assessment of AlphaFold3.bioRxiv : the preprint server for biology · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Systematic discovery of motif-based interactions of the auxiliary domains of USP family deubiquitinases.Nature communications · 2026Article
- FlyPredictome: A structural atlas of predicted protein-protein interactions inbioRxiv : the preprint server for biology · 2026Article
- LIVIA: a browser-based tool for assessing and visualizing predicted protein interactions.bioRxiv : the preprint server for biology · 2026Article
- AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores.Protein science : a publication of the Protein Society · 2026Article
- Bromodomain dimers: A case study of BRD4 and family-wide AlphaFold predictions.Structural dynamics (Melville, N.Y.) · 2026Article
- The C16orf87 protein is a subunit of the MIER corepressor complex controlling embryonic development and cell migration.Scientific reports · 2026Article
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes.Bioinformatics (Oxford, England) · 2026Article
- Predicting the protein interaction landscape of a free-living bacterium with pooled-AlphaFold3.Molecular systems biology · 2026Article
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3 authors.
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Abstract
summaryOne of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions. The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interactions, in particular motif-mediated interactions, often also contain regions that remain flexible upon binding. These noninteracting flanking regions are assigned low confidence values and will affect ipTM, as it considers all interchain residue-residue pairs, and two models of the same motif-domain interaction, but differing in the length of their flanking regions, would be assigned very different values. Here, we propose actual interface pTM (actifpTM), a modified ipTM measure, that focuses on the residues participating in the interaction, resulting in a more robust measure of interaction confidence. Besides, actifpTM is calculated both for the full complex as well as for each pair of chains, making it well-suited for evaluating multi-chain complexes with a particularly critical binding interface, such as antibody-antigen interactions. AVAILABILITY AND IMPLEMENTATION: The method is available as part of the ColabFold (https://github.com/sokrypton/ColabFold) repository, installable both locally or usable with Colab notebook.
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